Abstract
<div class="section abstract"><div class="htmlview paragraph">Cars and vans are accountable for 14.5% of the total CO2 emissions in the European Union, exerting a significant impact on public health and the environment. To align with the climate objectives set by the Council and the European Parliament, the Fit for 55 package encompasses a series of proposals aimed at revising and modernizing EU legislation while introducing new initiatives. The ultimate goal is to ensure that EU policies are in harmony with the climate targets, specifically the EU’s aspiration to reduce greenhouse gases (GHGs) by at least 55% by 2030 compared to 1990 levels and achieve climate neutrality by 2050. To meet the fleet average emissions targets, automotive Original Equipment Manufacturers (OEMs) are compelled to reduce emissions from their vehicles by addressing various components. The urgent need for car makers to reduce their carbon footprint, combined with the imperative to improve the mileage range of electric vehicles, has led to the creation of a novel methodology.</div><div class="htmlview paragraph">This approach focuses on optimizing car wheel bearing performance, with a particular emphasis on reducing friction. In contrast to traditional methodologies relying on Design of Experiments (DOE) investigations, the newly developed tool leverages an Archive-based Micro Genetic Algorithm (AMGA) for optimization. This algorithm excels in identifying the optimal bearing design within a constrained timeframe and significantly reduces the number of calculations required. Consequently, this innovation leads to a streamlined customer response process with a notably reduced lead time and fully customized design. The tool comprehensively improves bearing performance by evaluating the internal design geometry to assess ball set friction and identifying optimal dimensions for the seal design.</div><div class="htmlview paragraph">The objective of this paper is to delineate the comprehensive process involved in setting up and executing optimization, beginning with the identification of the most suitable algorithm. The paper further details the formulation of the optimization strategy tailored to meet customer requirements, highlights the key design factors identified for optimization and concludes by presenting the optimization results that underscore the potential for friction reduction. This holistic exploration covers the entire spectrum of optimization, offering insights into algorithm selection, strategy customization, and the tangible benefits achieved through friction reduction in the outcomes.</div></div>